Conducting On-Farm Experiments
Growers are constantly exposed to new information regarding innovative farming strategies, which may or may not be of benefit to their farming system. This is because the information is typically generalised and can’t be tailored towards a specific farming system or operation. It can often therefore be useful for growers to ‘ground-truth’ the change in practice on a small scale, before implementing it in full. In doing so, the grower can test the change in practice under their specific environmental (i.e. soil and climate) and operational (i.e. machinery and labour capacity) constraints. Whilst this method can work well, one of the difficult components to ground-truthing a change in practice, includes being able to accurately measure its impact. It is important to measure these impacts, to ensure that the decisions being made are of a net benefit to the overall farm business. To avoid making these assessments based on ‘gut-feel’, an on-farm experiment which produces objective data is often worthwhile.
Some examples of on-farm experiments in broadacre cropping include:
Side-by-side comparisons of different crop varieties.
Side-by-side comparison of different inputs. A good example would be applying a new pre-emergent herbicide to one part of a paddock and spraying the rest of the paddock with the incumbent product.
Strip-trialling of different input application rates. Examples include:
o High and low fertiliser application strips, particularly Nitrogen.
o Having a nil strip on a foliar fungicide pass.
Some examples of on-farm experiments in livestock-production include:
Splitting a mob of ewes based on pregnancy scans and evaluating the lambing and weaning result.
Comparing ‘top-shelf’ mineral supplements to basic lime/Causmag/salt supplements.
Conducting worm egg-count tests after drenching, to determine the effectiveness of drench active ingredients.
To ensure that the information produced from an on-farm experiment is useful, there are two main questions which need to be answered before conducting the experiment. These include:
1. How can the outcomes of the experiment be measured and does the measurement allow for an easy economic comparison?
2. How can a fair comparison be made?
It is important that the first question can be answered, because accurate data collection is a key component to drawing conclusions from an on-farm experiment. From a broadacre cropping perspective, the best method to measure the outcomes of an on-farm experiment involves using spatial yield data. With a side-by-side comparison, if the approximate location of the experiment is noted, then yield maps can be used to determine if any differences in productivity are present. Yield data also allows for simple economic analyses to be conducted, meaning that in most instances, it is the most important measuring tool for an on-farm cropping experiment. Depending on the input being used in the experiment, other sources of data such as satellite imagery, spatial protein data and/or soil testing, might also be good sources of information which can be used to assess the outcomes of an experiment.
From a livestock production perspective, consistent and accurate record keeping is likely to be the best way to measure an output-based on-farm experiment. For example, having accurate records of ewe and lamb numbers at lamb-marking and weaning, allows for the quantification of the impacts of splitting a scanned mob of ewes into ‘singles’ and ‘twins.’ Another example includes weighing calves at weaning and then again prior to selling, to allow for growth-rate comparisons when conducting a mineral supplement trial. Both of these examples allow for a simple economic analysis to be conducted. In any case, whether in broadacre cropping or livestock production, it is important to consider how the outcomes of an on-farm experiment will be measured prior to conducting the trial.
The second main question (i.e. How can a fair comparison be made?) is also important, because the number of uncontrolled variables in an on-farm experiment needs to be minimised. For instance, if conducting a side-by-side crop variety trial, it is important to ensure that both varieties are treated the same and grown in the same soil type and topography, to make a fair comparison. For broadacre cropping, it is relatively easy to ensure that most variables, other than those being tested, are kept constant throughout the trial. For livestock production, however, this can be more difficult. For instance, if conducting a mob-based trial, it is important to try and ensure that there are not substantial differences in animal age, genetics and/or feed on offer. If compromises have to be made in order to conduct the trial, then it is important that these are acknowledged when assessing the results. Ultimately, minimising the number of uncontrolled variables in an on-farm experiment, gives the person conducting the experiment confidence that the results are due to the variable being deliberately changed.
On-farm experiments offer the opportunity to validate a change in practice or input use pattern, under the specific conditions within a particular farming system. The ability to be able to quantify the impact of a change in practice, allows for decisions to be made with real data rather than assumptions.